Computer Science > Neural and Evolutionary Computing

Title:
What Happened to My Dog in That Network: Unraveling Top-down Generators in Convolutional Neural Networks

Abstract: Top-down information plays a central role in human perception, but plays
relatively little role in many current state-of-the-art deep networks, such as
Convolutional Neural Networks (CNNs). This work seeks to explore a path by
which top-down information can have a direct impact within current deep
networks. We explore this path by learning and using "generators" corresponding
to the network internal effects of three types of transformation (each a
restriction of a general affine transformation): rotation, scaling, and
translation. We demonstrate how these learned generators can be used to
transfer top-down information to novel settings, as mediated by the "feature
flows" that the transformations (and the associated generators) correspond to
inside the network. Specifically, we explore three aspects: 1) using generators
as part of a method for synthesizing transformed images --- given a previously
unseen image, produce versions of that image corresponding to one or more
specified transformations, 2) "zero-shot learning" --- when provided with a
feature flow corresponding to the effect of a transformation of unknown amount,
leverage learned generators as part of a method by which to perform an accurate
categorization of the amount of transformation, even for amounts never observed
during training, and 3) (inside-CNN) "data augmentation" --- improve the
classification performance of an existing network by using the learned
generators to directly provide additional training "inside the CNN".